Guides

AI in market analysis, explained

13 plain, honest guides on the techniques, their limits, and the risks. Educational only, not financial advice.

AI & ML foundations

How is machine learning actually used in market analysis?A grounded overview of where ML fits, and the many places it does not.What is the difference between supervised and unsupervised learning?The two broad families of machine learning, explained with market examples.

Signals & indicators

What is a trading signal, really?The plain meaning of the term, and the gap between a signal and a promise.How does an AI focused market research firm typically work?A neutral look at the kinds of services firms in this space describe offering.How does AI based analysis differ from traditional technical analysis?Where the two overlap, where they diverge, and why neither escapes market risk.

Data & features

What are features in a market model, and why do they matter?Features are the inputs a model learns from, and they decide the ceiling of what is possible.

Models & techniques

Which machine learning models are commonly used in market research?A plain tour of the main families and what each is suited to.How is sentiment analysis used on financial text?Turning news, filings, and posts into numbers, and the limits of doing so.

Risk, limits & pitfalls

What is data leakage, and why does it ruin market models?The single most common reason a model looks brilliant on paper and fails live.What is overfitting, and why do so many strategies fail live?When a model memorizes the past instead of learning something that lasts.What are the red flags of an AI trading claim?A practical checklist for reading any claim about an AI driven strategy or signal.

Evaluation & tools

What is backtesting, and how is it done honestly?Simulating a strategy on history, and all the ways it can quietly lie to you.How do you measure whether a market model is any good?The metrics researchers use, and why no single number tells the whole story.